




"""simple docstring"""




import torch

from transformers import AutoModel

class   UpperCAmelCase_    (			torch.nn.Module		):
		def __init__(						self				,						UpperCamelCase_="sayef/fsner-bert-base-uncased"       )						->       Optional[Any]:
					super(UpperCamelCase_				,						self       ).__init__()

					__lowercase			:  List[str]								=       AutoModel.from_pretrained(UpperCamelCase_				,						return_dict=UpperCamelCase_       )
					__lowercase			:  Any								=       torch.nn.CosineSimilarity(3				,						1E-08       )
					__lowercase			:  Optional[Any]								=       torch.nn.Softmax(dim=1       )
		def 	_lowerCamelCase							(						self				,						**UpperCamelCase_       )						->       Optional[Any]:
					return self.bert(**UpperCamelCase_       ).last_hidden_state
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_       )						->       Tuple:
					return token_embeddings.sum(2				,						keepdim=UpperCamelCase_       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_=1       )						->       Any:
					return self.softmax(T * self.cos(UpperCamelCase_				,						UpperCamelCase_       )       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_       )						->       List[str]:
					__lowercase			:  Tuple								=       W_supports['''sizes'''].tolist()
					__lowercase			:  Union[str, Any]								=       W_supports['''start_token_id'''].item()
					__lowercase			:  Dict								=       W_supports['''end_token_id'''].item()

					del W_supports["sizes"]
					del W_supports["start_token_id"]
					del W_supports["end_token_id"]

					__lowercase			:  Optional[int]								=       self.BERT(**UpperCamelCase_       )
					__lowercase			:  Union[str, Any]								=       self.BERT(**UpperCamelCase_       )

					__lowercase			:  List[Any]								=       None
					__lowercase			:  List[Any]								=       None

					__lowercase			:  Tuple								=       W_supports['''input_ids'''] == start_token_id
					__lowercase			:  str								=       W_supports['''input_ids'''] == end_token_id

					for i, size in enumerate(UpperCamelCase_       ):
								if i == 0:
											__lowercase			:  int								=       0
								else:
											__lowercase			:  Optional[Any]								=       support_sizes[i - 1]

								__lowercase			:  int								=       S[s : s + size][start_token_masks[s : s + size]]
								__lowercase			:  List[str]								=       S[s : s + size][end_token_masks[s : s + size]]

								__lowercase			:  Optional[Any]								=       torch.matmul(q[i]				,						s_start.T       ).sum(1       ).softmax(0       )
								__lowercase			:  List[str]								=       torch.matmul(q[i]				,						s_end.T       ).sum(1       ).softmax(0       )

								if p_starts is not None:
											__lowercase			:  Optional[Any]								=       torch.vstack((p_starts, p_start)       )
											__lowercase			:  Dict								=       torch.vstack((p_ends, p_end)       )
								else:
											__lowercase			:  Any								=       p_start
											__lowercase			:  int								=       p_end

					return p_starts, p_ends




from argparse import ArgumentParser

from .env import EnvironmentCommand





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				ArgumentParser('''Diffusers CLI tool'''				,     usage='''diffusers-cli <command> [<args>]'''				)
				__lowercase					     =				parser.add_subparsers(help='''diffusers-cli command helpers'''				)

				# Register commands
				EnvironmentCommand.register_subcommand(lowercase				)

				# Let's go
				__lowercase					     =				parser.parse_args()

				if not hasattr(lowercase				,     '''func'''				):
								parser.print_help()
								exit(1				)

				# Run
				__lowercase					     =				args.func(lowercase				)
				service.run()


if __name__ == "__main__":
		main()